DevFestMD ’17: a day of talks, workshops and networking, Fri Oct 27, Baltimore

Want to learn about blockchain or machine learning? Like to get hands-on experience building software for IoT? Participate in DevFestMD ’17 and do all of that and more! DevFestMD is a day-long tech event filled with talks and hands-on workshops. So whether you’re thinking about joining the tech community or a seasoned software engineer, they have something for you. Early Bird tickets are only $10 and includes breakfast and lunch.

DevFestMD takes place on Friday, October 27, at Betamore at City Garage, 101 West Dickman St., Baltimore

HackUMBC hackathon, Saturday-Sunday 7-8 October 2017

HackUMBC hackathon, Saturday-Sunday 7-8 October 2017

HackUMBC is a 24-hour tech innovation marathon where students across the East Coast collaborate on new ideas to build mobile, web and hardware projects. HackUMBC invites diverse groups of students, undergraduate, graduate and high school students over 18, to enjoy a weekend of hacking, workshops, tech talks, networking, and other fun activities. At the end of 24 hours, projects are presented and judged for different prize categories from sponsors and other organizations.

What if I don’t have a team or an idea?: No problem! You can find a team once you arrive. Most hackers arrive without a team. You will often find inspiration for ideas at the hackathon.

What if I don’t code?: This is the perfect opportunity to learn something new! There will be workshops geared towards beginners and mentors to help you throughout the event.

What can I build?: Anything! Web, mobile, desktop, and hardware projects are all welcome. Projects will be judged based on creativity, technical difficulty, polish, and usefulness.

Will there be hardware? HackUMBC has partnered with MLH to provide hardware hacking resources to all hackers. Check out the full list of hardware.

How much does it cost? HackUMBC is free! Food, beverages, swag, workspaces, and sleeping areas will be provided. You just have to travel to the event and we will take care of the rest!

The event starts in Meyerhoff 030 at 10:00am on Saturday, October 7 and ends at 3:30pm on Sunday, October 8. Visit the HackUMBC site for complete details and to register.

talk: Results from the SFS Summer Research Study on NetAdmin, 12p Fri 9/8

UMBC Cyber Defense Lab

Results from the SFS Summer Research Study at UMBC

Enis Golaszewski, UMBC

12:00–1:00pm, Friday, 8 September 2017
ITE 228 (or nearby), UMBC

In summer 2017, UMBC held a cybersecurity research workshop that featured the UMBC Scholarship For Service (SFS) cohort working with the cooperation of the UMBC Department of Information Technology (DoIT) to analyze the security of NetAdmin, a software tool developed and used by DoIT. The workshop included six new SFS scholars transferring to UMBC from Montgomery College and Prince George’s Community College and provided students with experience in analyzing the security of software while uncovering serious flaws in the NetAdmin tool. NetAdmin allows authorized research faculty at UMBC to make research servers running on campus accessible to connections originating from off-campus.

Because NetAdmin directly modifies the campus firewall, possible security weaknesses in its architecture, implementation, or usage could present a significant risk to UMBC computer systems. During the four-day study, students uncovered multiple critical security flaws and developed recommendations for mitigating them. These flaws include architectural weaknesses, injection attack vulnerabilities, and susceptibility to man-in-the-middle attacks. The workshop was successful for improving the security of NetAdmin as well as integrating the incoming SFS scholars with the existing UMBC cohort.

In this talk, we will focus on the technical details of our security analysis of the NetAdmin tool.

Enis Golaszewski is a PhD student and SFS scholar in computer science working with Dr. Sherman on protocol analysis and the security of software-defined networks. Email:

Host: Alan T. Sherman,

UMBC researchers develop AI system to design clothing for your personal fashion style

 

AI system designs clothing for your personal fashion style

Everyone knows that more and more data is being collected about our everyday activities, like where we go online and in the physical world. Much of that data is being used for personalization. Recent UMBC CSEE Masters student Prutha Date explored a novel kind of personalization – creating clothing that matches your personal style.

Date developed a system that takes as input pictures of clothing in your closet, extracts a digitial representation of your style preferences, and then applies that style to new articles of clothing, like a picture pair of pants or a dress you find online. This work meshes well with recent efforts by Amazon to manufacture clothing on demand. Imagine being able to click on an article of clothing available online, personalize it to your style, and then have it made and shipped right to your door!

This innovative research was cited in a recent article in MIT Technology Review, Amazon Has Developed an AI Fashion Designer.

Tim Oates, a professor at the University of Maryland in Baltimore County, presented details of a system for transferring a particular style from one garment to another. He suggests that this approach might be used to conjure up new items of clothing from scratch. “You could train [an algorithm] on your closet, and then you could say here’s a jacket or a pair of pants, and I’d like to adapt it to my style,” Oates says.

Fashion designers probably shouldn’t fret just yet, though. Oates and other point out that it may be a long time before a machine can invent a fashion trend. “People innovate in areas like music, fashion, and cinema,” he says. “What we haven’t seen is a genuinely new music or fashion style that was generated by a computer and really resonated with people.”

You can read more about the work in a recent paper by Prutha Date, Ashwinkumar Ganesan and Tim Oates, Fashioning with Networks: Neural Style Transfer to Design Clothes. The paper describes how convolutional neural networks were used to personalize and generate new custom clothes based on a person’s preference and by learning their fashion choices from a limited set of clothes from their closet.

PhD defense: Prajit Das, Context-dependent privacy and security management on mobile devices

Ph.D. Dissertation Defense

Context-dependent privacy and security management on mobile devices

Prajit Kumar Das

8:00-11:00am Tuesday, 22 August 2017, ITE325b, UMBC

There are ongoing security and privacy concerns regarding mobile platforms which are being used by a growing number of citizens. Security and privacy models typically used by mobile platforms use one-time permission acquisition mechanisms. However, modifying access rights after initial authorization in mobile systems is often too tedious and complicated for users. User studies show that a typical user does not understand permissions requested by applications or are too eager to use the applications to care to understand the permission implications. For example, the Brightest Flashlight application was reported to have logged precise locations and unique user identifiers, which have nothing to do with a flashlight application’s intended functionality, but more than 50 million users used a version of this application which would have forced them to allow this permission. Given the penetration of mobile devices into our lives, a fine-grained context-dependent security and privacy control approach needs to be created.

We have created Mithril as an end-to-end mobile access control framework that allows us to capture access control needs for specific users, by observing violations of known policies. The framework studies mobile application executables to better inform users of the risks associated with using certain applications. The policy capture process involves an iterative user feedback process that captures policy modifications required to mediate observed violations. Precision of policy is used to determine convergence of the policy capture process. Policy rules in the system are written using Semantic Web technologies and the Platys ontology to define a hierarchical notion of context. Policy rule antecedents are comprised of context elements derived using the Platys ontology employing a query engine, an inference mechanism and mobile sensors. We performed a user study that proves the feasibility of using our violation driven policy capture process to gather user-specific policy modifications.

We contribute to the static and dynamic study of mobile applications by defining “application behavior” as a possible way of understanding mobile applications and creating access control policies for them. Our user study also shows that unlike our behavior-based policy, a “deny by default” mechanism hampers usability of access control systems. We also show that inclusion of crowd-sourced policies leads to further reduction in user burden and need for engagement while capturing context-based access control policy. We enrich knowledge about mobile “application behavior” and expose this knowledge through the Mobipedia knowledge-base. We also extend context synthesis for semantic presence detection on mobile devices by combining Bluetooth, low energy beacons and Nearby Messaging services from Google.

Committee: Drs. Anupam Joshi (chair), Tim Finin (co-chair), Tim Oates, Nilanjan Banerjee, Arkady Zaslavsky, (CSIRO), Dipanjan Chakraborty (Shopperts)

PhD Defense: Bryan Wilkinson, Identifying and Ordering Scalar Adjectives using Lexical Substitution

Ph.D. Dissertation Defense

Identifying and Ordering Scalar Adjectives using Lexical Substitution

Bryan Wilkinson

1:00pm Friday, 18 August 2017, ITE 325b, UMBC

Lexical semantics provides many important resources in natural language processing, despite the recent preferences for distributional methods. In this dissertation we investigate an under-represented lexical relationship, that of scalarity. We define sclarity as it relates to adjectives and introduce novel methods to identify words belonging to a particular scale and to order those words once they are found. This information has important uses in both traditional linguistics as well as natural language processing. We focus on solving both these problems using lexical substitution, a technique that allows us to determine the best substitute word for a given word in a sentence. We also produce two new datasets: a gold standard of scalar adjectives for use in the development and evaluation of methods like the ones introduces here, and a test set of indirect question-answer pairs, one possible application of scalar adjectives.

Committee: Drs. Tim Oates, CSEE (Chair), Charles Nicholas, Tim Finin, Shimei Pan (IS) and Mona Diab (GWU CS)

UMBC PhD candidate Kavita Krishnaswamy gets Google & Microsoft awards for robotics research

 

UMBC Ph.D. candidate Kavita Krishnaswamy receives
Google and Microsoft awards for robotics research

 

Kavita Krishnaswamy ’07, computer science and mathematics, Ph.D. ’18, computer science, has been named both a 2017 Microsoft Fellow and recipient of the Google Lime Scholarship. These prestigious honors recognize emerging scholars in computing who are dedicated to increasing diversity in the field, and Krishnawamy’s awards will support her Ph.D. research on “Smart Algorithms via Knowledge Management of Safe Physical Human-Robotic Care.”

“I am very humbled, honored, and grateful to Google Lime and Microsoft for providing me with this enriching experience and lifetime opportunity to serve society by advancing the field of human-robot interaction,” Krishnaswamy says.

The Google Lime Scholarship seeks to promote greater access to knowledge for people with visible and invisible disabilities. It was established through a partnership between Google and Lime Connect, a nonprofit focused on breaking stereotypes about disability and encouraging companies to recognize the importance and value of employing people with disabilities.

The program encourages students with disabilities to pursue their passions in computing and technology, and to become leaders in those fields. As part of the fellowship program, Krishnaswamy will receive scholarship funding and will participate in the 2017 Google Scholars’ Retreat.

Through the Microsoft Fellowship, Krishnaswamy will receive funding to support her Ph.D. research and will participate in the Microsoft Research workshop held in fall 2017. Her research currently focuses on building a teleoperated mobile robotic prototype, in addition to creating an accessible robotic interface, that seniors and people with disabilities will be able to control by repositioning their arms and legs. Tim Oates, professor of computer science and electrical engineering, is Krishnaswamy’s Ph.D. advisor.

“Our goal is to explore the intersection between providing physical care and robotics, and how it is possible to translate safe patient handling and mobility guidelines into smart human-robotic interaction algorithms,” Krishnaswamy explains. “As assistive robotics become more mainstream, these best practices can improve safety in direct physical care in the process of repositioning the human body with a mobile robotic arm.”

Krishnaswamy is excited about the possible new directions her research can now explore, thanks to support from these awards. “The resources will provide me with a solid and steady foundation to cultivate new technical expertise and professional skills to successfully continue my dissertation research in robotics, and to broaden my knowledge in the field,” she says.

Krishnaswamy has been recognized internationally as an emerging leader in robotics and accessibility design throughout her graduate studies. She is a former Ford Foundation Predoctoral Fellow, and a National Science Foundation Graduate Research Fellow. In 2015, she was named to Robohub’s “25 Women in Robotics You Need to Know About” list.

This post was adapted from a UMBC News article written by Megan Hanks. Image by Kavita Krishnaswamy.

PhD Defense: The Lightweight Virtual File System

Dissertation Defense

The Lightweight Virtual File System

Navid Golpayegani

10:00-12:00 Thursday, 20 July 2017, ITE 325, UMBC

 

A data center today is responsible for safely managing big data volumes and balancing the complex needs between data producers and consumers. This balance often involves reconciling the needs of easy access and rapid retrieval in ways desired by the consumers with the needs of long term availability, reliability, and expandability of data producers. The long term continuous support of data storage adds another layer of complexity for the file system. As storage architecture and big data volumes evolve, existing file system’s primary focus is performance while less attention is payed to addressing the problems of the above long term servicing needs of their clients.

I have developed the Lightweight Virtual File System (LVFS) to address these problems through the unique conceptual approach of separating the most common tasks involved in a file system; namely storing data, locating data, and organizing data. Standard file systems are developed as single monolithic systems performing all three tasks. LVFS replaces these tasks with an architecture which enables the dynamic combination of different algorithms for each of those tasks. Using this approach, LVFS is capable of constructing a storage system, which allows for ready availability, reliability, expandability, and long term support while, simultaneously, assuring the performance of a stable system customizable to meet the needs of data consumers.

After successful development and testing to allow for merging decades old storage architecture with new and incompatible ones, such as HGST Active Archive System, NASA Goddard Space Flight Center’s Terrestrial Information Systems Laboratory adopted LVFS for their production environment to create a single, integrated storage system without any software modifications. UMBC’s Center for Hybrid Multicore Productivity Research deployed an instance on the IBM iDataPlex ‘BlueWave’ cluster to utilize Seagate’s Active Drive systems as a storage and on-disk compute platform. With LVFS we show we were able to perform MapReduce computation directly on the drive with comparable performance to Hadoop running on BlueWave. It also shows a significant reduction in data leaving the active drive during computation thereby significantly increasing throughput.

Committee Members: Dr.s Milton Halem (Advisor), Yelena Yesha, John Dorband, Charles Nicholas, Curt Tilmes

PhD defense: Deep Representation of Lyrical Style and Semantics for Music Recommendation

Dissertation Defense

Deep Representation of Lyrical Style and Semantics for Music Recommendation

Abhay L. Kashyap

11:00-1:00 Thursday, 20 July 2017, ITE 346

In the age of music streaming, the need for effective recommendations is important for music discovery and a personalized user experience. Collaborative filtering based recommenders suffer from popularity bias and cold-start which is commonly mitigated by content features. For music, research in content based methods have mainly been focused in the acoustic domain while lyrical content has received little attention. Lyrics contain information about a song’s topic and sentiment that cannot be easily extracted from the audio. This is especially important for lyrics-centric genres like Rap, which was the most streamed genre in 2016. The goal of this dissertation is to explore and evaluate different lyrical content features that could be useful for content, context and emotion based models for music recommendation systems.

With Rap as the primary use case, this dissertation focuses on featurizing two main aspects of lyrics; its artistic style of composition and its semantic content. For lyrical style, a suite of high level rhyme density features are extracted in addition to literary features like the use of figurative language, profanity and vocabulary strength. In contrast to these engineered features, Convolutional Neural Networks (CNN) are used to automatically learn rhyme patterns and other relevant features. For semantics, lyrics are represented using both traditional IR techniques and the more recent neural embedding methods.

These lyrical features are evaluated for artist identification and compared with artist and song similarity measures from a real-world collaborative filtering based recommendation system from Last.fm. It is shown that both rhyme and literary features serve as strong indicators to characterize artists with feature learning methods like CNNs achieving comparable results. For artist and song similarity, a strong relationship was observed between these features and the way users consume music while neural embedding methods significantly outperformed LSA. Finally, this work is accompanied by a web-application, Rapalytics.com, that is dedicated to visualizing all these lyrical features and has been featured on a number of media outlets, most notably, Vox, attn: and Metro.

Committee: Drs. Tim Finin (chair), Anupam Joshi, Tim Oates, Cynthia Matuszek and Pranam Kolari (Walmart Labs)

PhD Proposal: Analysis of Irregular Event Sequences using Deep Learning, Reinforcement Learning & Visualization

Analysis of Irregular Event Sequences using Deep Learning, Reinforcement Learning, and Visualization

Filip Dabek

11:00-1:00 Thursday 13 July 2017, ITE 346, UMBC

History is nothing but a catalogued series of events organized into data. Amazon, the largest online retailer in the world, processes over 2,000 orders per minute. Orders come from customers on a recurring basis through subscriptions or as one-off spontaneous purchases, resulting in each customer exhibiting their own behavioral pattern when it comes to the way in which they place orders throughout the year. For a company such as Amazon, that generates over $130 billion of revenue each year, understanding and uncovering the hidden patterns and trends within this data is paramount in improving the efficiency of their infrastructure ranging from the management of the inventory within their warehouses, distribution of their labor force, and preparation of their online systems for the load of users. With the ever increasingly availability of big data, problems such as these are no longer limited to large corporations but are experienced across a wide range of domains and faced by analysts and researchers each and every day.

While many event analysis and time series tools have been developed for the purpose of analyzing such datasets, most approaches tend to target clean and evenly spaced data. When faced with noisy or irregular data, it has been recommended to undergo a pre-processing step of converting and transforming the data into being regular. This transformation technique arguably interferes on a fundamental level as to how the data is represented, and may irrevocably bias the way in which results are obtained. Therefore, operating on raw data, in its noisy natural form, is necessary to ensure that the insights gathered through analysis are accurate and valid.

In this dissertation novel approaches are presented for analyzing irregular event sequences using a variety of techniques ranging from deep learning, reinforcement learning, and visualization. We show how common tasks in event analysis can be performed directly on an irregular event dataset without requiring a transformation that alters the natural representation of the process that the data was captured from. The three tasks that we showcase include: (i) summarization of large event datasets, (ii) modeling the processes that create events, and (iii) predicting future events that will occur.

Committee: Drs. Tim Oates (Chair), Jesus Caban, Penny Rheingans, Jian Chen, Tim Finin

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